Extended Trigger Terms for Extracting Adverse Drug Reactions in Social Media Texts
- 1 Palestine Technical University, Palestinian Authority
- 2 Universiti Kebangsaan Malaysia, Malaysia
Copyright: © 2020 Rami Naim Mohammad Yousef, Sabrina Tiun and Nazlia Omar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Adverse Drug Reaction (ADR) is a disorder caused by taking medications. Studies have addressed extracting ADRs from social networks where users express their opinion regarding a specific medication. Extracting entities mainly depends on specific terms called trigger terms that may occur before or after ADRs. However, these terms should be extended, especially when examining multiple representation of N-gram. This study aims to propose an extension of trigger terms based on the multiple representation of N-gram. Two benchmark datasets are used in the experiments and three classifiers, namely, support vector machine, Naïve Bayes and linear regression, are trained on the proposed extension. Furthermore, two document representations have been utilized including Term Frequency Inverse Document Frequency (TFIDF) and Count Vector (CV). Results show that the proposed extended trigger terms outperform the baseline by achieving 88% and 69% of F1-scores for the first and second datasets, respectively. This finding implies the effectiveness of the proposed extended trigger terms in terms of detecting new ADRs.
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- Adverse Drug Reaction
- Trigger Terms
- Feature Extraction
- Support Vector Machine
- Linear Regression